Transforming Education: A Comprehensive Review of Generative Artificial Intelligence in Educational Settings through Bibliometric and Content Analysis

Abstract

In the ever-evolving era of technological advancements, generative artificial intelligence (GAI) emerges as a transformative force, revolutionizing education. This review paper, guided by the PRISMA framework, presents a comprehensive analysis of GAI in education, synthesizing key insights from a selection of 207 research papers to identify research gaps and future directions in the field. This study begins with a content analysis that explores GAI’s transformative impact in specific educational domains, including medical education and engineering education. The versatile applications of GAI encompass assessment, personalized learning support, and intelligent tutoring systems. Ethical considerations, interdisciplinary collaboration, and responsible technology use are highlighted, emphasizing the need for transparent GAI models and addressing biases. Subsequently, a bibliometric analysis of GAI in education is conducted, examining prominent AI tools, research focus, geographic distribution, and interdisciplinary collaboration. ChatGPT emerges as a dominant GAI tool, and the analysis reveals significant and exponential growth in GAI research in 2023. Moreover, this paper identifies promising future research directions, such as GAI-enhanced curriculum design and longitudinal studies tracking its long-term impact on learning outcomes. These findings provide a comprehensive understanding of GAI’s potential in reshaping education and offer valuable insights to researchers, educators, and policymakers interested in the intersection of GAI and education.

Creative Learning for Sustainability in a World of AI: Action, Mindset, Values

“…Incorporating GenAI into sustainability education opens up a wealth of creative possibilities for engaging students to better understand the complex ecological dynamics and interdependencies that are often difficult to grasp. These could range from developing interactive simulations of ecosystems, modeling the effects of climate change, and exploring human-environment interactions and more [26]. Warr and colleagues [27] highlight AI's capacity for analytical tasks, noting its ability to detect patterns and apply them in novel contexts, revealing new insights in environmental studies that might otherwise be overlooked.…”

Generative Artificial Intelligence in Tertiary Education: Assessment Redesign Principles and Considerations

“…Thus, the assessment triangle provides some foundations and considerations to help achieve quality education through fit-for-purpose assessment [23]. To this end, consideration of the purpose of assessment is especially important in the era of generative AI, where educators and stakeholders would need to rethink the following: (1) intended program learning outcomes; (2) program-related assessments, whether or not they are still relevant and required; (3) what knowledge or skills need to be retained; and (4) what types of assessment outputs need to be produced.…”

“…Within the education landscape and particularly in tertiary education institutions, generative AI has impacted how students learn, how teachers teach [3][4][5][6] and how assessments are conducted [6]. For instance, given access, students can now use ChatGPT or something similar to generate essays, reports, academic and scientific research papers, programming codes and creative content.…”

Using ChatGPT to Support Lesson Planning for the Historical Experiments of Thomson, Millikan, and Rutherford

“…An early investigation using ChatGPT-3.5 to answer chemistry questions found that the chatbot had strong natural language processing skills (NLP) to communicate information, but that the accuracy of the chatbot’s output was a concern . Shortcomings involving the accuracy of ChatGPT-3.5 have since been discussed in early stage reviews of the use of Artificial Intelligence (AI) in educational settings, − leading some to conclude that inaccuracies are a serious impediment to its use. , Others have looked closely at the nature of these errors and considered them in relation to student reasoning and problem-solving. Fergus and co-workers found that ChatGPT’s answering of questions involving the interpretation of nontext information and the application of knowledge were weaknesses for the chatbot .…”